How AI Is Impacting Business Management in the Oil and Gas Sector

A few weeks ago, I made a site visit to a semi-submersible platform. Quite a bit of time had passed since I had last spent more than a day on a vessel or rig. The plan was to spend a couple of days but due to adverse weather conditions, I practically spent a week offshore. I should mention that my background is in finance and I held CFO and Country Manager positions. For the first half of my career, working for two of the top international accounting and audit firms. In the last 18 years, I have been involved in the energy sector, having the opportunity to learn a lot of "engineering stuff" and attaining a broader knowledge of the energy industry.

While at the platform, I talked to many valuable people in different positions, including technicians, mechanics, electricians, HSE and crew, and I must say, I admire the way most of these people find solutions to different problems, often with limited resources, as well as how they address problems with a level of certainty of getting things done one way or another, usually overcoming the problem with genius and very imaginative solutions.  At lunch one day, a Norwegian colleague said  that sometimes we can have an idea of the business we lead but seeing how people behave and focus on making the right calls is where really the money comes from.

In the last few weeks, artificial intelligence (AI) solutions have ramped up on a mass scale. These tools have become more accessible to improve not only business but basically every single aspect of life. It is not  new, as we already use AI in many business processes and increasingly in the O&G industry, but, lately, AI basically has become accessible to anyone with access to the internet. As  AI becomes even more capable through  use, feedback, and, especially, on-the-go auto-learning, I’d like to share some thoughts on how AI is impacting and will impact the O&G industry over the coming months and years.

My  first thought is related to my two initial paragraphs and whether AI is a threat to take over the jobs of many people? The answer is yes but no. In the first instance, automatization of certain equipment is reducing headcounts and remote operations require less involvement of people while leading to more efficient operations. An example would be an ROV system installed on a rig in the Gulf of Mexico and driven by operators in a control center located in Aberdeen, who can also manage different systems in different places at the same time. On the other hand, fewer people in the field means less exposure to risk and does not necessarily mean fewer jobs. For me, it also means different types of jobs in the future and a call to evolve the way we manage talent and to increase the technical capabilities within our teams.

Unlike other sectors (renewables, financial, healthcare, for example), the O&G industry started a bit later with the adoption of new technology, including AI, but it is now a reality in the day-to-day. We do many things using AI now:

• Predictive maintenance, by analyzing data from sensors on equipment, such as drilling equipment, pumps, turbines and compressors,  to detect potential failures before they occur, and then adjust maintenance programs.

• Risk management, by analyzing available data related to natural disasters, political instability, price fluctuations, satellite images, actual and historical weather statistics, which helps to mitigate risks to the operations in a much more proactive and predictive way so plans and measures can be adjusted accordingly to reduce exposure.

• Production optimization, by analyzing different factors, such as O&G reserves, market demand, and weather patterns, which helps to maximize production while reducing costs.

• Exploration. For instance, seismic and geological data is always huge, hundreds of terabytes of information must be analyzed and interpreted to confirm potential and identify drilling locations to optimize rates of success. This is now assisted by AI to expedite analysis and increase the chances of success.

• Social impact. AI can be used to analyze customer behavior, feedback, and sentiment as well as gather information about the communities where we operate to gain insights into social impact, helping to identify social issues that can then be addressed properly and proactively.

• Governance. The use of AI helps to monitor and analyze compliance with regulatory requirements, laws, privacy regulations and anti-corruption laws, among others. This can produce predictive information for potential risks and opportunities for improvement and to draft appropriate policies.

The above are just a few examples of the actual use of AI in the O&G industry; therefore, we are no longer talking about potential benefits but an actual reality that will change rapidly in the coming years if companies embrace and adapt to this new reality of AI as part of our lives and work.

As you can see, many things are now and will be managed by the use of AI but this does not mean human interaction, and especially many jobs, will be replaced. It means we will have to adjust to the new reality,  getting the best from technology while incentivizing creativity in our talented people to evolve our business. Also bear in mind that the O&G market will have  relatively high prices for many years while  simultaneously existing with the energy transition, so investment in the sector is still significant.

Embracing AI as part of a company’s evolution can be a significant step forward for improving efficiency, decision-making, and customer experience, but for O&G companies this requires a strategic approach that considers the unique needs and challenges of each company, with proper planning and execution. In this way, companies can leverage AI to gain a competitive edge and drive innovation.

Here are some steps that a company can take to leverage AI:

1. Identify potential use cases. We need to analyze where AI can be applied to solve business problems or improve operations and automatize a process. Use AI to sort valuable information from the bulk set of information, among other uses.

2. Develop a strong data foundation. To benefit from AI, we need to be sure we have a solid base of data, with enough quality and accuracy. This is difficult to achieve as we tend to think we have a lot of data, CRM systems, accounting systems, QHSE systems, production data, and so on, but having tons of data is not the same as having all that information properly organized and connected.

3. Talent investment. After the pandemic, we understood that people in an organization require a set of technical skills to manage their day-to-day, either working remotely or at the office. We also learned that a leader's profile changed from a focus on traditional models where technical/market knowledge and financial awareness were prominent to now require a whole set of human skills, such as communication and motivational capabilities. We  also need to think in terms of skills that can help to design, develop, and deploy AI solutions. 

4. Scalability. We need to consider that deploying AI into our processes is not a big one-off  effort and then manage from there. It requires a scalability approach where we start with small pilot projects, test, and gain experience on the go so we can deploy AI solutions gradually, moving to  larger projects with more successful results.

5. Ethics is another big discussion topic, and specific articles have been written regarding the ethical aspects of AI, but at this time, let's just consider that implementation of AI triggers ethical implications; for instance, the use of private information and the security of that information. Therefore, a set of guidelines and policies are necessary to ensure that AI is used responsibly.

6. Continuous learning process. We must be constantly updated on the latest AI trends, which are rapidly evolving, and companies should be up-to-date to ensure they are leveraging the latest technology.

For the many challenges the O&G industry is facing, such as changing demand for energy, volatility of prices, government regulations, environmental impact, cybersecurity, access to resources, aging of infrastructure, competition, security, and politics, the use of AI will become part of the solution, helping us with decision-making, expediting processes and helping to leverage value and efficiency, ultimately pumping up profits by rationalizing resources and costs.

And finally, just as an example of how AI is a present reality, to write this article, I used AI to help me suggest some of the addressed items. It is not yet good enough to conduct the main idea and to write the article for me, but it is good enough to suggest information and organize items by asking the right questions. I even had to correct some of the AI’s responses that did not consider a source that I had in mind. After I told the AI it was missing this source, it apologized and immediately checked the link I shared and corrected its response, using the new information and an auto-learning feature. That is impressive. I’m sure if I ask how to fix certain problems in a real situation in the field it probably will help with some suggestions but definitively, I still rely much more on the operator with a name (and of course a nickname) and who has been there for many years and can fix a problem based on experience. Let’s see how far this goes in the future.